Laia Subirats

Circles of Health

OBJECTIVES This research is concerned with the study of a new social-network platform, which (1) provides people with disabilities of neurological origin, their relatives, health professionals, therapists, carers and institutions with an interoperable platform that supports standard indicators, (2) promotes knowledge democratization and user empowerment, and (3) allows making decisions with a more informed opinion. METHODS A new social network, Circles of Health, has been designed, developed and tested by end-users. To allow monitoring the evolution of peoples health status and comparing it with other users and with their cohort, anonymized data of 2675 people from comprehensive and multidimensional medical evaluations, carried out yearly from 2006 to 2010, have been standardized to the International Classification of Functioning, Disability and Health, integrated into the corresponding medical health records and then used to automatically generate and graphically represent multidimensional indicators. These indicators have been integrated into Circles of Healths social environment, which has been then evaluated via expert and user-experience analyses. RESULTS Patients used Circles of Health to exchange bio-psycho-social information (medical and otherwise) about their everyday lives. Health professionals remarked that the use of color-coding in graphical representations is useful to quickly diagnose deficiencies, difficulties or barriers in rehabilitation. Most people with disabilities complained about the excessive amount of information and the difficulty in interpreting graphical representations. CONCLUSIONS Health professionals found Circles of Health useful to generate a more integrative understanding of health based on a comprehensive profile of individuals instead of being focused on patients diseases and injuries. People with disabilities found enriching personal knowledge with the experiences of other users helpful. The number of descriptors used at the same time in the graphical interface should be reduced in future versions of the social-network platform.

Knowledge Representation for Prognosis of Health Status in Rehabilitation

Abstract: In this article, key points are discussed concerning knowledge representation for clinical decision support systems in the domain of physical medicine and rehabilitation. Information models, classifications and terminologies, such as the “virtual medical record” (vMR), the “international classification of functioning, disability and health” (ICF), the “international classification of diseases” (ICD) and the “systematized nomenclature of medicine—clinical terms” (SNOMED CT), are used for knowledge integration and reasoning. A system is described that supports the measuring of functioning status, diversity, prognosis and similarity between patients in the post-acute stage, thus helping health professionals’ prescription of recommendations. Keywords: representation methods; clinical decision support systems; knowledge systems; rehabilitation; information models; classifications; terminologies 1. Introduction In the domain of medicine in general, and physical medicine and rehabilitation in particular, several standard terminologies and classifications exist [1] that can be used for knowledge representation and integration. Some examples are: the

On Semantic, Rule-Based Reasoning in the Management of Functional Rehabilitation Processes

A clinical decision support system, based on rules described in the semantic web rule language and with semantic annotations from biomedical and time ontologies, is used to reason on processes modeled in the business process modeling notation. This paper, as a case study within the framework of functional rehabilitation processes, analyzes the modeling of the rehabilitation activity consisting of improving the upper limb functioning of patients. The clinical decision support system provides personalization of therapies and is powerful enough to deal with the special characteristics of a rehabilitation scenario, which includes several types of indicators, medical ontologies, and time annotations of different granularities. This paper presents the main lines of a rule-based, ontological framework to translate informal, descriptive methods about functional rehabilitation with an intuitive semantics to the formal representation needed by computational systems. A rule-based reasoning system is used for the representation of processes’ semantics and the modeling categories are based on well-accepted rehabilitation notions. We believe that the solution presented for functional rehabilitation can be generalized to other rehabilitation domains such as respiratory, cognitive and cardiac rehabilitation.

An ontology for computer-based decision support in rehabilitation

Although functionality and disease classifications are available thanks to initiatives such as the “international classification of functioning, disability and health”, the “systematized nomenclature of medicine - clinical terms” and the “international classification of diseases”, a formal model of rehabilitation interventions has not been defined yet. This model can have a fundamental role in the design of computer-based decision support in rehabilitation. Some initiatives such as the “international classification of health interventions” are in development, but their scope is overly general to cope with the specificities that characterize rehabilitation. The aim of this work is to represent knowledge in order to carry out diagnosis and personalization of activities in cases of people with functional diversity. To define the diagnosis and activity personalization, a methodology has been developed to extract standardized concepts from clinical scales and the literature.

Monitoring and Prognosis System Based on the ICF for People with Traumatic Brain Injury

The objective of this research is to provide a standardized platform to monitor and predict indicators of people with traumatic brain injury using the International Classification of Functioning, Disability and Health, and analyze its potential benefits for people with disabilities, health centers and administrations. We developed a platform that allows automatic standardization and automatic graphical representations of indicators of the status of individuals and populations. We used data from 730 people with acquired brain injury performing periodic comprehensive evaluations in the years 2006–2013. Health professionals noted that the use of color-coded graphical representation is useful for quickly diagnose failures, limitations or restrictions in rehabilitation. The prognosis system achieves 41% of accuracy and sensitivity in the prediction of emotional functions, and 48% of accuracy and sensitivity in the prediction of executive functions. This monitoring and prognosis system has the potential to: (1) save costs and time, (2) provide more information to make decisions, (3) promote interoperability, (4) facilitate joint decision-making, and (5) improve policies of socioeconomic evaluation of the burden of disease. Professionals found the monitoring system useful because it generates a more comprehensive understanding of health oriented to the profile of the patients, instead of their diseases and injuries.

Automatic Assessment of Socioeconomic Impact on Cardiac Rehabilitation

Disability-Adjusted Life Years (DALYs) and Quality-Adjusted Life Years (QALYs), which capture life expectancy and quality of the remaining life-years, are applied in a new method to measure socioeconomic impacts related to health. A 7-step methodology estimating the impact of health interventions based on DALYs, QALYs and functioning changes is presented. It relates the latter (1) to the EQ-5D-5L questionnaire (2) to automatically calculate the health status before and after the intervention (3). This change of status is represented as a change in quality of life when calculating QALYs gained due to the intervention (4). In order to make an economic assessment, QALYs gained are converted to DALYs averted (5). Then, by inferring the cost/DALY from the cost associated to the disability in terms of DALYs lost (6) and taking into account the cost of the action, cost savings due to the intervention are calculated (7) as an objective measure of socioeconomic impact. The methodology is implemented in Java. Cases within the framework of cardiac rehabilitation processes are analyzed and the calculations are based on 200 patients who underwent different cardiac-rehabilitation processes. Results show that these interventions result, on average, in a gain in QALYs of 0.6 and a cost savings of 8,000 €.

Mining Facebook Data of People with Rare Diseases

This research is concerned with the study of Spanish Facebook pages that deal with rare diseases. The objectives of this research are to characterise these pages and to compare them with the priorities of the Decalogue of the Spanish Federation of Rare Diseases (FEDER). This research uses Netvizz to download the data, word clouds in R to perform text mining, TextBlob in Python to perform sentiment analysis, and log-likelihood in R to compare Facebook and Decalogue words. The results obtained show that photos are the type of posts with higher number of likes, reactions and engagement. We can also see that positive polarities have higher level of engagement, and that subjectivity is not so correlated with engagement. In the comparison of the Facebook data with the FEDER Decalogue, we observe that the following words have a lot of presence in the Decalogue and little in Facebook: disability, professionals and diseases. Similarly, these are the most present on Facebook with little representation in the Decalogue: help, life, people and children. In conclusion, we can say that the Decalogue should focus more on help, life, people and children and less on disability, professionals and diseases.

Medical-treatment Recommendation and the Integration of Process Models into Knowledge-based Systems

Decision making based on evidence other than human reasoning is becoming increasingly important in healthcare. Much valuable evidence is in the form of the treatment processes used by healthcare institutions, and in their meta-analyses. This paper presents a new framework for representing and synthesizing knowledge from treatment processes, and discusses the role and integration of this knowledge in case-based reasoning systems. With respect to patient status, as single instants cannot convey sufficient information, time series are analyzed and classified to improve decision-making ability. We aim at the elicitation of new knowledge that is valuable for improving case-based reasoning steps, taking into account international standards, ontologies, information models, nomenclatures and multiple types of indicators. The integration of formal process-modeling in case-based reasoning is exemplified by a real-world application scenario. After evaluation with a medical-rehabilitation data set, results show a strong correspondence between treatment recommended by the proposed system and clinical practice

Artificial Intelligence and Earth Observation to Explore Water Quality in the Wadden Sea

Earth-observation systems (satellites and in situ monitoring) are routinely used to collect information about water quality. Recently, smartphone-based tools and other citizen-science sensors have enabled citizens to also contribute to the collection of scientifically relevant data. This chapter describes a decision support system used to predict optical water-quality indicators in the Wadden Sea, which is an intertidal marine system, where natural processes related to sediment transport and primary production define the basis of its ecological values. As information sources, the system uses satellite data, data collected with a mobile app and physical data for the period 2003–2015. An artificial-intelligence technique, inductive learning, is used to analyze the data and provide predictions in terms of water colour represented via the Forel-Ule scale (a comparative scale for colour).

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis. The Facebook analysis shows that posts with photos and positive comments have the highest engagement. We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue. Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day.